There are several topics related to Databricks that you can study. Here are a few options:
Big Data Analysis: Learn how to work with large datasets efficiently using Databricks' unified analytics platform.
Apache Spark: Dive deep into Apache Spark, the open-source big data processing framework that powers Databricks, and understand its architecture, components, and advanced features.
Data Engineering: Master the skills needed to design, build, and maintain data pipelines using Databricks to handle various data sources, transformations, and integrations.
Machine Learning with PySpark: Explore how to leverage Databricks and PySpark libraries to implement machine learning algorithms for tasks such as classification, regression, clustering, and recommendation systems.
Data Visualization with Databricks: Learn how to create interactive and insightful visualizations using Databricks' collaborative environment, enabling you to gain valuable insights from your data.
Cloud Computing: Understand how Databricks utilizes cloud computing technologies like AWS, Azure, or GCP to provide scalable and performant data processing and analytics solutions.
- Data Science with Databricks: Develop skills in data exploration, feature engineering, model training, and evaluation using Databricks' collaborative environment along with popular data science libraries like Pandas, NumPy, and scikit-learn.
These topics cover a range of skills and knowledge related to Databricks, allowing you to improve your proficiency in big data analytics, data engineering, machine learning, and cloud-based data solutions.‎
